André Carlos Ponce de Leon Ferreira de Carvalho
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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation for the last research proposal) Birthplace: Brazil
Full Professor at the Institute of Mathematical and Computer Sciences, University of São Paulo (ICMC-USP), Campus São Carlos, CNPq Research Productivity Fellow 1A and Vice-President of the Brazilian Computer Society (SBC). He holds a Bachelor's degree (1987) and a Master's Degree in Computer Science (1990) from the Federal University of Pernambuco, and a PhD in Electronic Engineering from the University of Kent (1994). His main research interests are Machine Learning, Data Mining and Data Science, with applications in various areas. Author of several books, including Inteligência Artificial: Uma abordagem de Aprendizado de Máquina, published by GrupoGen in 2011 and Jabuti Award in 2012, and A General Introduction to Data Analytics, published by Wiley, in 2018. Evaluates projects for national research funding agencies, CAPES, CNPq, FAPESP, FACEPE, FAPEMIG and Fundação Serrapilheira, and international, NSERC (Canada), The Leverhulme Trust (UK), EPSRC UK), GACR (Czech Republic), CONICYT (Chile) and MIUR (Italy). He is deputy director of ICMC-USP and the Center for Applied Mathematical Sciences for Industry at USP. He is a member of the Computer Science Advisory Committee (CA-CC), of the Science for Education Network (CpE), the International Network for Government Science Advice (INGSA), of the Technical Scientific Council of the School of Applied Mathematics of the Getúlio Vargas Foundation (EMAp-FGV), of the University Council of the Universidade de São Paulo, of the Advanced Institute for Artificial Intelligence (AI2) and Member of the Strategy and Partnerships Board of the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI (ART-AI), at University of Bath, UK. Once a month he writes about Artificial Intelligence on the Science & Mathematics Blog, IMPA and OGlobo. (Source: Lattes Curriculum)
| News published in Agência FAPESP Newsletter about the researcher |
| Mathematics as a tool to make the use of big data more representative |
- Mobility in urban computing: characterization, modeling and applications (MOBILIS), AP.TEM
Abstract
Mobility plays a central role in various related activities we conduct, especially in work, commerce, industry and leisure. The study of mobility for different purposes (e.g., economics, public services, academic study) should be done in a systematic way since from the scientific point of view it is the most fundamental issue that must be studied, so proper solutions can be proposed. Th...
- Advanced machine learning, AP.PITE
Abstract
Without being aware, we are using technologies based on Machine learning (ML) algorithms in a growing number of our daily activities. The use of ML has made many risk and tiring activities safer, more reliable and more accurate. Despite these contributions, new demands require the development of new ML algorithms, or use of these algorithms in new and innovative ways. Two important curr...
- CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID
Abstract
The focus of this proposal is the transfer of mathematical knowledge to other areas of science, technology and industry, through a structured research center specially tailored for this purpose. All mathematical knowledge is ultimately applicable, if not directly, through other areas of knowledge. In some areas the application of mathematics is almost immediate. However, the practical i...
- Symposium on Knowledge Discovery, Mining and Learning, AO.R
- Metalearning for algorithm selection, AV.EXT
Abstract
This international cooperation project describes the activities to be performed by Prof. Dr. Carlos M. M. O. P. Soares, from the School of Engineering of University of Porto and INESC TEC, during his visit to ICMC-USP. In this visit he will work in a scientific cooperation in the field of Data Mining, specifically in Meta-learning. Prof. Carlos Soares will also present a research semina...
- Learning from data streams, AV.EXT
Abstract
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. All of them learn decision models that continuously evolve over time, run in resource-aware environments, detect and react to changes in the environment generating data. In this project we will focus on the research associated with the application of ...
(Only some records are available in English at this moment)
- Meta-learning applied to search deep neural network architectures, BP.DR
Abstract
Deep learning has enabled several breakthroughs in complex tasks such as object detection, image segmentation, image classification, and ultra-realistic imaging. Such progress was possible due to the massive amount of data available nowadays and the hardware resources now capable of processing such data in an optimized way. Another important point was the feature extraction mechanisms i...
- Web platform development and support for AutoML and curated data, BP.TT
- Machine learning tools for bioinformatics problems, BP.DR
Abstract
In the recent years, machine learning techniques have been extensively used for bioinformatics, due to their capacity in solving hard problems by learning a function from a set of known examples which is able to make predictions for new and unseen data. Motivated by such results we will tackle in this project three different bioinformatics problems using machine learning techniques: (i)...
(Only some records are available in English at this moment)
- Treatment of imbalanced data for lncRNA classification, BP.IC
Abstract
Recent years have contributed to increased interest in studying long non-coding RNAs (lncRNA). With great consistency, lncRNA has shown its importance on several types of genomic regulation, and, therefore, it is correlated with the development of several biological processes and diseases - among them cancer. To better understand the functions of this kind of RNA and their mechanisms, s...
- Efficiency of metaheuristics in the context of feature selection in gene expression database, BP.IC
Abstract
Studies of various pathologies, such as cancer, may benefit from technologies capable of capturing the expression of genes in tissues, such as RNA-Seq and Microarray, which can correlate the development of the disease with the patient's gene's expression. However, working with a large volume of data creates new challenges. In this context, the feature selection, the pre-processing of th...
- Use of meta-learning for clustering algorithm selection problems, BP.PD
Abstract
Clustering is one of the main Machine Learning tasks. There are several data clustering techniques and different problems are different techniques. Choosing an algorithm in a non-automated way can be costly and requires in-depth knowledge of the problem and algorithms. In this way, meta-learning emerges as a tool to automate the process of algorithm selection. The proposal of this proje...
(Only some records are available in English at this moment)
- Meta-learning applied to imbalanced datasets using data complexity measures, BE.EP.DR
Abstract
Several works have investigated the effect of data imbalance on the performance of predictive models. Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalance data poses a difficulty for the minority class w...
- Ensemble of classiers for unbalanced data sets, BE.EP.DR
Abstract
In many practical classification problems, the data set used for the induction of a classifier is significantly unbalanced. This occurs when the number of instances of a particular class is much lower than the number of instances in the other(s) class(es). Unbalanced data sets can compromise the performance of most classical classification algorithms, since these algorithms assume a bal...
- Use of Meta-learning to improve deep learning algorithms in classification problems, BE.EP.DR
Abstract
Machine learning approaches have been widely effective solving many simple and well de- fined problems in the literature. However, most approaches face difficulties when dealing with more complicated real-world problems, often due to high data dimensionality. One of the first applications of Deep Learning models was with data dimensionality reduction. These learning models have attracte...
- Metaheuristics for label noise identification in classification tasks, BE.EP.DD
Abstract
The successful application of machine learning techniques to data sets from many areas requires the dedication of a considerable amount of time to identify and treat noisy data. Noisy data is a frequent problem regarding information collection, transmission and storage. The presence of noise in data can affect the complexity of classification tasks, making the discrimination of objects ...
(Only some records are available in English at this moment)
| 3 / 3 | Ongoing research grants |
| 24 / 13 | Completed research grants |
| 11 / 11 | Ongoing scholarships in Brazil |
| 53 / 26 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 9 / 9 | Completed scholarships abroad |
| 101 / 63 | All research grants and scholarships |
| Associated processes | |
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
| Publications | 21 |
| Citations | 60 |
| Cit./Article | 2.9 |
| Data from Web of Science | |
ESPEZUA, SOLEDAD; VILLANUEVA, EDWIN; MACIEL, CARLOS D.; CARVALHO, ANDRE. A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets. Neurocomputing, v. 149, n. B, p. 767-776, FEB 3 2015. Web of Science Citations: 10. (12/22295-0)
VALLIM, ROSANE M. M.; ANDRADE FILHO, JOSE A.; DE MELLO, RODRIGO F.; DE CARVALHO, ANDRE C. P. L. F.; GAMA, JOAO. Unsupervised density-based behavior change detection in data streams. Intelligent Data Analysis, v. 18, n. 2, p. 181-201, 2014. Web of Science Citations: 3. (10/11250-0)
LORENA, ANA C.; CARVALHO, ANDRÉ C. P. L. F. DE. Evaluation of noise reduction techniques in the splice junction recognition problem. GENETICS AND MOLECULAR BIOLOGY, v. 27, n. 4, p. 665-672, Dec. 2004.
GIAMPAOLO LUIZ LIBRALÃO; ANTONIO VALERIO NETTO; ANDRÉ PONCE DE LEON F. DE CARVALHO; MARIA CRISTINA FERREIRA DE OLIVEIRA. Determinação de vícios refrativos oculares utilizando Support Vector Machines. Sba : Controle & Automação, v. 16, n. 2, p. 146-158, Jun. 2005.
AGUIAR, GABRIEL JONAS; MANTOVANI, RAFAEL GOMES; MASTELINI, SAULO M.; DE CARVALHO, ANDRE C. P. F. L.; CAMPOS, GABRIEL F. C.; BARBON JUNIOR, SYLVIO. A meta-learning approach for selecting image segmentation algorithm. PATTERN RECOGNITION LETTERS, v. 128, p. 480-487, DEC 1 2019. Web of Science Citations: 0. (16/18615-0, 12/23114-9, 13/07375-0, 18/07319-6)
CENTINI CAMPOS, GABRIEL FILLIPE; MASTELINI, SAULO MARTIELLO; AGUIAR, GABRIEL JONAS; MANTOVANI, RAFAEL GOMES; DE MELO, LEONIMER FLAVIO; BARBON, JR., SYLVIO. Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization. EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 6 2019. Web of Science Citations: 1. (12/23114-9)
GARCIA, LUIS P. F.; LEHMANN, JENS; DE CARVALHO, ANDRE C. P. L. F.; LORENA, ANA C.. New label noise injection methods for the evaluation of noise filters. KNOWLEDGE-BASED SYSTEMS, v. 163, p. 693-704, JAN 1 2019. Web of Science Citations: 0. (16/18615-0, 13/07375-0, 12/22608-8)
MANTOVANI, RAFAEL G.; ROSSI, ANDRE L. D.; ALCOBACA, EDESIO; VANSCHOREN, JOAQUIN; DE CARVALHO, ANDRE C. P. L. F.. A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers. INFORMATION SCIENCES, v. 501, p. 193-221, OCT 2019. Web of Science Citations: 0. (12/23114-9, 15/03986-0, 18/14819-5)
SPADON, GABRIEL; DE CARVALHO, ANDRE C. P. L. F.; RODRIGUES-JR, JOSE F.; ALVES, LUIZ G. A.. Reconstructing commuters network using machine learning and urban indicators. SCIENTIFIC REPORTS, v. 9, AUG 13 2019. Web of Science Citations: 0. (16/17078-0, 17/08376-0, 19/04461-9, 13/07375-0, 16/16987-7, 16/18615-0, 14/25337-0)
FRIAS-BLANCO, ISVANI; DEL CAMPO-AVILA, JOSE; RAMOS-JIMENEZ, GONZALO; CARVALHO, ANDRE C. P. L. F.; ORTIZ-DIAZ, AGUSTIN; MORALES-BUENO, RAFAEL. Online adaptive decision trees based on concentration inequalities. KNOWLEDGE-BASED SYSTEMS, v. 104, p. 179-194, JUL 15 2016. Web of Science Citations: 3. (15/03355-0)
VALLIM, ROSANE M. M.; ANDRADE FILHO, JOSE A.; DE MELLO, RODRIGO F.; DE CARVALHO, ANDRE C. P. L. F.; GAMA, JOAO. Unsupervised density-based behavior change detection in data streams. Intelligent Data Analysis, v. 18, n. 2, p. 181-201, 2014. Web of Science Citations: 3. (10/11250-0)
LORENA, ANA C.; CARVALHO, ANDRÉ C. P. L. F. DE. Evaluation of noise reduction techniques in the splice junction recognition problem. GENETICS AND MOLECULAR BIOLOGY, v. 27, n. 4, p. 665-672, Dec. 2004.
GIAMPAOLO LUIZ LIBRALÃO; ANTONIO VALERIO NETTO; ANDRÉ PONCE DE LEON F. DE CARVALHO; MARIA CRISTINA FERREIRA DE OLIVEIRA. Determinação de vícios refrativos oculares utilizando Support Vector Machines. Sba : Controle & Automação, v. 16, n. 2, p. 146-158, Jun. 2005.
AGUIAR, GABRIEL JONAS; MANTOVANI, RAFAEL GOMES; MASTELINI, SAULO M.; DE CARVALHO, ANDRE C. P. F. L.; CAMPOS, GABRIEL F. C.; BARBON JUNIOR, SYLVIO. A meta-learning approach for selecting image segmentation algorithm. PATTERN RECOGNITION LETTERS, v. 128, p. 480-487, DEC 1 2019. Web of Science Citations: 0. (16/18615-0, 12/23114-9, 13/07375-0, 18/07319-6)
CENTINI CAMPOS, GABRIEL FILLIPE; MASTELINI, SAULO MARTIELLO; AGUIAR, GABRIEL JONAS; MANTOVANI, RAFAEL GOMES; DE MELO, LEONIMER FLAVIO; BARBON, JR., SYLVIO. Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization. EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 6 2019. Web of Science Citations: 1. (12/23114-9)
GARCIA, LUIS P. F.; LEHMANN, JENS; DE CARVALHO, ANDRE C. P. L. F.; LORENA, ANA C.. New label noise injection methods for the evaluation of noise filters. KNOWLEDGE-BASED SYSTEMS, v. 163, p. 693-704, JAN 1 2019. Web of Science Citations: 0. (16/18615-0, 13/07375-0, 12/22608-8)
MANTOVANI, RAFAEL G.; ROSSI, ANDRE L. D.; ALCOBACA, EDESIO; VANSCHOREN, JOAQUIN; DE CARVALHO, ANDRE C. P. L. F.. A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers. INFORMATION SCIENCES, v. 501, p. 193-221, OCT 2019. Web of Science Citations: 0. (12/23114-9, 15/03986-0, 18/14819-5)
SPADON, GABRIEL; DE CARVALHO, ANDRE C. P. L. F.; RODRIGUES-JR, JOSE F.; ALVES, LUIZ G. A.. Reconstructing commuters network using machine learning and urban indicators. SCIENTIFIC REPORTS, v. 9, AUG 13 2019. Web of Science Citations: 0. (16/17078-0, 17/08376-0, 19/04461-9, 13/07375-0, 16/16987-7, 16/18615-0, 14/25337-0)
FRIAS-BLANCO, ISVANI; DEL CAMPO-AVILA, JOSE; RAMOS-JIMENEZ, GONZALO; CARVALHO, ANDRE C. P. L. F.; ORTIZ-DIAZ, AGUSTIN; MORALES-BUENO, RAFAEL. Online adaptive decision trees based on concentration inequalities. KNOWLEDGE-BASED SYSTEMS, v. 104, p. 179-194, JUL 15 2016. Web of Science Citations: 3. (15/03355-0)
PISANI, PAULO HENRIQUE; POH, NORMAN; DE CARVALHO, ANDRE C. P. L. F.; LORENA, ANA CAROLINA. Score normalization applied to adaptive biometric systems. COMPUTERS & SECURITY, v. 70, p. 565-580, SEP 2017. Web of Science Citations: 2. (13/07375-0, 12/25032-0, 12/22608-8)
PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. F.. A new data characterization for selecting clustering algorithms using meta-learning. INFORMATION SCIENCES, v. 477, p. 203-219, MAR 2019. Web of Science Citations: 2. (16/18615-0, 13/07375-0, 17/20265-0)
BARROS, RODRIGO C.; BASGALUPP, MARCIO P.; DE CARVALHO, ANDRE C. P. L. F.. Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification. Genetic Programming and Evolvable Machines, v. 16, n. 3, p. 241-281, SEP 2015. Web of Science Citations: 1. (09/14325-3)
BARROS, RODRIGO C.; BASGALUPP, MARCIO P.; FREITAS, ALEX A.; DE CARVALHO, ANDRE C. P. L. F.. Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, v. 18, n. 6, p. 873-892, DEC 2014. Web of Science Citations: 18. (09/14325-3)
(References retrieved automatically from State of São Paulo Research Institutions)
CERRI, Ricardo. Técnicas de classificação hierárquica multirrótulo. 2010. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
MEDEIROS, Debora Maria Rossi de. Análise de dados por meio de agrupamento fuzzy semi-supervisionado e mineração de textos. 2010. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
DINIZ, Helio. Integração de Redes Neurais Artificiais & Métodos Estocásticos para Previsão de Séries Temporais. 1999. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
FERNANDES, Everlandio Rebouças Queiroz. Comitês evolucionários para aprendizado desbalanceado. 2018. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
CERRI, Ricardo. Redes neurais e algoritmos genéticos para problemas de classificação hierárquica multirrótulo. 2013. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
ROSSI, André Luis Debiaso. Meta-aprendizado aplicado a fluxos contínuos de dados. 2013. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BARELLA, Victor Hugo. Técnicas para o problema de dados desbalanceados em classificação hierárquica. 2015. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
SOVAT, Ricardo Barz. Uma abordagem híbrida baseada em casos e redes neurais. Uma aplicação: escolha e configuração de modelos de redes neurais. 2002. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
GARCIA, Luís Paulo Faina. Detecção de ruídos em problemas de classificação. 2016. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
VALLIM, Rosane Maria Maffei. Mineração de fluxos contínuos de dados para jogos de computador. 2013. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BASGALUPP, Márcio Porto. LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão. 2010. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
CERRI, Ricardo. Técnicas de classificação hierárquica multirrótulo. 2010. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
MEDEIROS, Debora Maria Rossi de. Análise de dados por meio de agrupamento fuzzy semi-supervisionado e mineração de textos. 2010. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
DINIZ, Helio. Integração de Redes Neurais Artificiais & Métodos Estocásticos para Previsão de Séries Temporais. 1999. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
FERNANDES, Everlandio Rebouças Queiroz. Comitês evolucionários para aprendizado desbalanceado. 2018. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
CERRI, Ricardo. Redes neurais e algoritmos genéticos para problemas de classificação hierárquica multirrótulo. 2013. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
ROSSI, André Luis Debiaso. Meta-aprendizado aplicado a fluxos contínuos de dados. 2013. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BARELLA, Victor Hugo. Técnicas para o problema de dados desbalanceados em classificação hierárquica. 2015. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
SOVAT, Ricardo Barz. Uma abordagem híbrida baseada em casos e redes neurais. Uma aplicação: escolha e configuração de modelos de redes neurais. 2002. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
GARCIA, Luís Paulo Faina. Detecção de ruídos em problemas de classificação. 2016. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
VALLIM, Rosane Maria Maffei. Sistemas classificadores evolutivos para problemas multirrótulo. 2009. 85f. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
ROSSET, Mariá Cristina Vasconcelos Nascimento. Metaheurísticas para o problema de agrupamento de dados em grafo. 2010. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BIANCHI, Rodrigo Elias. Extração de conhecimento simbólico em técnicas de aprendizado de máquina caixa-preta por similaridade de rankings. 2008. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BASGALUPP, Márcio Porto. LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão. 2010. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.